We also compare our NN-Degree-based accuracy predictor with the current state-of-the-art approaches. As shown in Table REF , most of the existing approaches use Graph-based neural networks to make predictions [1]}, [2]}, [3]}, [4]}. However, Graph-based neural networks require much more training data, and they are much more complicated in terms of computation and model structure compared to classical methods like logistic regression. Due to the significant reduction in the model complexity, our predictor requires \(6.88\times \)  fewer training samples, although a much larger search space (\(1.5\times 10^5\)  larger than the existing work) is covered. Moreover, our NN-Degree based predictor has only three parameters to be updated; hence it consumes \(66\times \)  less fine-tuning time than the existing approaches. Finally, besides such low model complexity and fast training process, our predictor achieves a very small RMSE (0.152%) as well.
